Mathematics of Machine Learning and Industrial Applications II. 2 credits

Catalog description

Basic mathematical and probabilistic models and derivations for convolutions, stability, regularization, inverse and optimal control problems, and dynamical systems in the context of semi-supervised learning used in artificial intelligence (AI). Mathematical and numerical aspects of stochastic descent methods, Nesterov accelerated gradient, AdaGrad, Adam, with applications to convolutional, deep, and ODE networks. Further applications include imaging and computer vision, saliency maps, segmentation, satellite Imagery, and physics informed learning. Offered by Mathematics . Limited to three attempts.